Title: Electricity price forecasting with Recurrent Neural Networks TensorFlow-KR 2016.06.18

1. Taegyun Jeon
TensorFlow-KR / 2016.06.18
Gwangju Institute of Science and Technology
Electricity Price Forecasting
with Recurrent Neural Networks
RNN을 이용한 전력 가격 예측
TensorFlow-KR Advanced Track

2. Who is a speaker?
 Taegyun Jeon (GIST)
▫ Research Scientist in Machine Learning and Biomedical Engineering
tgjeon@gist.ac.kr
linkedin.com/in/tgjeon
tgjeon.github.io
Page 2[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
 Github for this tutorial:
https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series

3. What you will learn about RNN
How to:
 Build a prediction model
▫ Easy case study: sine function
▫ Practical case study: electricity price forecasting
 Manipulate time series data
▫ For RNN models
 Run and evaluate graph
 Predict using RNN as regressor
Page 3[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

4. Contents
 Overview of TensorFlow
 Recurrent Neural Networks (RNN)
 RNN Implementation
 Case studies
▫ Case study #1: sine function
▫ Case study #2: electricity price forecasting
 Conclusions
 Q & A
Page 4[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

5. Contents
 Overview of TensorFlow
 Recurrent Neural Networks (RNN)
 RNN Implementation
 Case studies
▫ Case study #1: sine function
▫ Case study #2: electricity price forecasting
 Conclusions
 Q & A
Page 5[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

6. TensorFlow
 Open Source Software Library for Machine Intelligence
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 6

7. Prerequisite
 Software
▫ TensorFlow (r0.9)
▫ Python (3.4.4)
▫ Numpy (1.11.0)
▫ Pandas (0.16.2)
 Tutorials
▫ “Recurrent Neural Networks”, TensorFlow Tutorials
▫ “Sequence-to-Sequence Models”, TensorFlow Tutorials
 Blog Posts
▫ Understanding LSTM Networks (Chris Olah @ colah.github.io)
▫ Introduction to Recurrent Networks in TensorFlow (Danijar Hafner @ danijar.com)
 Book
▫ “Deep Learning”, I. Goodfellow, Y. Bengio, and A. Courville, MIT Press, 2016
Page 7[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

8. Contents
 Overview of TensorFlow
 Recurrent Neural Networks (RNN)
 RNN Implementation
 Case studies
▫ Case study #1: sine function
▫ Case study #2: electricity price forecasting
 Conclusions
 Q & A
Page 8[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

9. Recurrent Neural Networks
 Neural Networks
▫ Inputs and outputs are independent
Page 9[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
 Recurrent Neural Networks
▫ Sequential inputs and outputs
...
𝑥 𝑥 𝑥
𝑜
𝑠𝑠
𝑠𝑠
𝑜 𝑜
...
𝑥𝑡−1 𝑥𝑡 𝑥𝑡+1
𝑜𝑡−1
𝑠𝑠
𝑠𝑠
𝑜𝑡 𝑜𝑡+1

10. Recurrent Neural Networks (RNN)
 𝒙 𝒕: the input at time step 𝑡
 𝒔 𝒕: the hidden state at time 𝑡
 𝒐 𝒕: the output state at time 𝑡
Page 10[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
Image from WILDML.com: “RECURRENT NEURAL NETWORKS TUTORIAL, PART 1 – INTRODUCTION TO RNNS”

11. Overall procedure: RNN
 Initialization
▫ All zeros
▫ Random values (dependent on activation function)
▫ Xavier initialization [1]:
Random values in the interval from −
1
𝑛
,
1
𝑛
,
where n is the number of incoming connections
from the previous layer
Page 11[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
[1] X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks” (2010)

12. Overall procedure: RNN
 Initialization
 Forward Propagation
▫ 𝑠𝑡 = 𝑓 𝑈𝑥 𝑡 + 𝑊𝑠𝑡−1
• Function 𝑓 usually is a nonlinearity such as tanh or ReLU
▫ 𝑜𝑡 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝑉𝑠𝑡
Page 12[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

13. Overall procedure: RNN
 Initialization
 Forward Propagation
 Calculating the loss
▫ 𝑦: the labeled data
▫ 𝑜: the output data
▫ Cross-entropy loss:
𝐿 𝑦, 𝑜 = −
1
𝑁 𝑛∈𝑁 𝑦𝑛log(𝑜 𝑛)
Page 13[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

14. Overall procedure: RNN
 Initialization
 Forward Propagation
 Calculating the loss
 Stochastic Gradient Descent (SGD)
▫ Push the parameters into a direction that reduced the error
▫ The directions: the gradients on the loss
:
𝜕𝐿
𝜕𝑈
,
𝜕𝐿
𝜕𝑉
,
𝜕𝐿
𝜕𝑊
Page 14[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

15. Overall procedure: RNN
 Initialization
 Forward Propagation
 Calculating the loss
 Stochastic Gradient Descent (SGD)
 Backpropagation Through Time (BPTT)
▫ Long-term dependencies
→ vanishing/exploding gradient problem
Page 15[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

16. Vanishing gradient over time
 Conventional RNN with sigmoid
▫ The sensitivity of the input values
decays over time
▫ The network forgets the previous input
 Long-Short Term Memory (LSTM) [2]
▫ The cell remember the input as long as
it wants
▫ The output can be used anytime it wants
[2] A. Graves. “Supervised Sequence Labelling with Recurrent Neural Networks” (2012)
Page 16[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

17. Design Patterns for RNN
 RNN Sequences
Page 17[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
Blog post by A. Karpathy. “The Unreasonable Effectiveness of Recurrent Neural Networks” (2015)
Task Input Output
Image classification fixed-sized image fixed-sized class
Image captioning image input sentence of words
Sentiment analysis sentence positive or negative sentiment
Machine translation sentence in English sentence in French
Video classification video sequence label each frame

18. Design Pattern for Time Series Prediction
Page 18[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
RNN
DNN
Linear Regression

19. Contents
 Overview of TensorFlow
 Recurrent Neural Networks (RNN)
 RNN Implementation
 Case studies
▫ Case study #1: sine function
▫ Case study #2: electricity price forecasting
 Conclusions
 Q & A
Page 19[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

20. RNN Implementation using TensorFlow
 How we design RNN model
for time series prediction?
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 20
 How manipulate our time
series data as input of RNN?

21. Regression models in Scikit-Learn
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 21
X = np.atleast_2d([0., 1., 2., 3., 5., 6., 7., 8., 9.5]).T
y = (X*np.sin(x)).ravel()
x = np.atleast_2d(np.linspace(0, 10, 1000)).T
gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4,
thetaU=1e-1, random_start=100)
gp.fit(X, y)
y_pred, MSE = gp.predict(x, eval_MSE=True)

22. RNN Implementation
 Recurrent States
▫ Choose RNN cell type
▫ Use multiple RNN cells
 Input layer
▫ Prepare time series data as RNN input
▫ Data splitting
▫ Connect input and recurrent layers
 Output layer
▫ Add DNN layer
▫ Add regression model
 Create RNN model for regression
▫ Train & Prediction
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 22

23. 1) Choose the RNN cell type
 Neural Network RNN Cells (tf.nn.rnn_cell)
▫ BasicRNNCell (tf.nn.rnn_cell.BasicRNNCell)
• activation : tanh()
• num_units : The number of units in the RNN cell
▫ BasicLSTMCell (tf.nn.rnn_cell.BasicLSTMCell)
• The implementation is based on RNN Regularization[3]
• activation : tanh()
• state_is_tuple : 2-tuples of the accepted and returned states
▫ GRUCell (tf.nn.rnn_cell.GRUCell)
• Gated Recurrent Unit cell[4]
• activation : tanh()
▫ LSTMCell (tf.nn.rnn_cell.LSTMCell)
• use_peepholes (bool) : diagonal/peephole connections[5].
• cell_clip (float) : the cell state is clipped by this value prior to the cell output activation.
• num_proj (int): The output dimensionality for the projection matrices
Page 23[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
[3] W. Zaremba, L. Sutskever, and O. Vinyals, “Recurrent Neural Network Regularization” (2014)
[4] K. Cho et al., “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation” (2014)
[5] H. Sak et al., “Long short-term memory recurrent neural network architectures for large scale acoustic modeling” (2014)

24. LAB-1) Choose the RNN Cell type
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 24
Import tensorflow as tf
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(num_units)
rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units)
rnn_cell = tf.nn.rnn_cell.GRUCell(num_units)
rnn_cell = tf.nn.rnn_cell.LSTMCell(num_units)
BasicRNNCell BasicLSTMCell
GRUCell LSTMCell

25. 2) Use the multiple RNN cells
▫ RNN Cell wrapper (tf.nn.rnn_cell.MultiRNNCell)
• Create a RNN cell composed sequentially of a number of RNN Cells.
▫ RNN Dropout (tf.nn.rnn_cell.Dropoutwrapper)
• Add dropout to inputs and outputs of the given cell.
▫ RNN Embedding wrapper (tf.nn.rnn_cell.EmbeddingWrapper)
• Add input embedding to the given cell.
• Ex) word2vec, GloVe
▫ RNN Input Projection wrapper (tf.nn.rnn_cell.InputProjectionWrapper)
• Add input projection to the given cell.
▫ RNN Output Projection wrapper (tf.nn.rnn_cell.OutputProjectionWrapper)
• Add output projection to the given cell.
Page 25[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

26. LAB-2) Use the multiple RNN cells
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 26
rnn_cell = tf.nn.rnn_cell.DropoutWrapper
(rnn_cell, input_keep_prob=0.8, output_keep_prob=0.8)
GRU/LSTM
Input_keep_prob=0.8
output_keep_prob=0.8
GRU/LSTM
GRU/LSTM
GRU/LSTM
Stacked_lstm = tf.nn.rnn_cell.MultiRNNCell([rnn_cell] * depth)
depth

27. 3) Prepare the time series data
 Split raw data into train, validation, and test dataset
▫ split_data [6]
• data : raw data
• val_size : the ratio of validation set (ex. val_size=0.2)
• test_size : the ratio of test set (ex. test_size=0.2)
Page 27[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
[6] M. Mourafiq, “tensorflow-lstm-regression” (code: https://github.com/mouradmourafiq/tensorflow-lstm-regression)
def split_data(data, val_size=0.2, test_size=0.2):
ntest = int(round(len(data) * (1 - test_size)))
nval = int(round(len(data.iloc[:ntest]) * (1 - val_size)))
df_train, df_val, df_test = data.iloc[:nval], data.iloc[nval:ntest],
data.iloc[ntest:]
return df_train, df_val, df_test

28. LAB-3) Prepare the time series data
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 28
train, val, test = split_data(raw_data, val_size=0.2, test_size=0.2)
Raw data
(100%)
Train
(80%)
Validation
(20%)
Test
(20%)
Test
(20%)
Train
(80%)
16%64% 20%

29. 3) Prepare the time series data
 Generate sequence pair (x, y)
▫ rnn_data [6]
• labels : True for input data (x) / False for target data (y)
• num_split : time_steps
• data : our data
Page 29[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
def rnn_data(data, time_steps, labels=False):
"""
creates new data frame based on previous observation
* example:
l = [1, 2, 3, 4, 5]
time_steps = 2
-> labels == False [[1, 2], [2, 3], [3, 4]]
-> labels == True [3, 4, 5]
"""
rnn_df = []
for i in range(len(data) - time_steps):
if labels:
try:
rnn_df.append(data.iloc[i + time_steps].as_matrix())
except AttributeError:
rnn_df.append(data.iloc[i + time_steps])
else:
data_ = data.iloc[i: i + time_steps].as_matrix()
rnn_df.append(data_ if len(data_.shape) > 1 else [[i] for i in data_])
return np.array(rnn_df)

30. LAB-3) Prepare the time series data
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 30
time_steps = 10
train_x = rnn_data(df_train, time_steps, labels=false)
train_y = rnn_data(df_train, time_steps, labels=true)
df_train [1:10000]
x #01
[1, 2, 3, …,10]
y #01
11
…
…
train_x
train_y
x #02
[2, 3, 4, …,11]
y #02
12
x #9990
[9990, 9991, 9992, …,9999]
y #9990
10000

31. 4) Split our data
 Split time series data into smaller tensors
▫ split (tf.split)
• split_dim : batch_size
• num_split : time_steps
• value : our data
▫ split_squeeze (tf.contrib.learn.ops.split_squeeze)
• Splits input on given dimension and then squeezes that dimension.
• dim
• num_split
• tensor_in
Page 31[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

32. LAB-4) Split our data
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 32
time_step = 10
x_split = split_squeeze(1, time_steps, x_data)
split_squeeze
1 2 3 10 𝑥𝑡−9 𝑥𝑡−8 𝑥 𝑡−7 … 𝑥𝑡
…
x #01
[1, 2, 3, …,10]

33. 5) Connect input and recurrent layers
 Create a recurrent neural network specified by RNNCell
▫ rnn (tf.nn.rnn)
• Args:
◦ cell : an instance of RNNCell
◦ inputs : list of inputs, tensor shape = [batch_size, input_size]
• Returns:
◦ (outputs, state)
◦ outputs : list of outputs
◦ state : the final state
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 33

34. LAB-5) Connect input and recurrent layers
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 34
rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units)
stacked_lstm = tf.nn.rnn_cell.MultiRNNCell([rnn_cell] * depth)
x_split = tf.split(batch_size, time_steps, x_data)
output, state = tf.nn.rnn(stacked_lstm, x_split)
𝑥𝑡−9 𝑥𝑡−8 𝑥𝑡−7 … 𝑥𝑡
LSTM
LSTM
LSTM
LSTM
LSTM
LSTM
LSTM
LSTM
LSTM
LSTM
LSTM
LSTM
…
𝑜𝑡−9 𝑜𝑡−8 𝑜𝑡−7 … 𝑜𝑡

35. 6) Output Layer
 Add DNN layer
▫ dnn (tf.contrib.learn.ops.dnn)
• input_layer
• hidden units
 Add Linear Regression
▫ linear_regression (tf.contrib.learn.models.linear_regression)
• X
• y
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 35

36. LAB-6) Output Layer
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 36
dnn_output = dnn(rnn_output, [10, 10])
LSTM_Regressor = linear_regression(dnn_output, y)
LSTM LSTM LSTM LSTM…
DNN Layer 1 with 10 hidden units
DNN Layer 2 with 10 hidden units
Linear regression

37. 7) Create RNN model for regression
 TensorFlowEstimator (tf.contrib.learn.TensorFlowEstimator)
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 37
regressor =
learn.TensorFlowEstimator(model_fn=LSTM_Regressor,
n_classes=0, verbose=1, steps=TRAINING_STEPS, optimizer='Adagrad',
learning_rate=0.03, batch_size=BATCH_SIZE)
regressor.fit(X['train'], y['train']
predicted = regressor.predict(X['test'])
mse = mean_squared_error(y['test'], predicted)

38. Contents
 Overview of TensorFlow
 Recurrent Neural Networks (RNN)
 RNN Implementation
 Case studies
▫ Case study #1: sine function
▫ Case study #2: electricity price forecasting
 Conclusions
 Q & A
Page 38[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

39. Case study #1: sine function
 Libraries
▫ numpy: package for scientific computing
▫ matplotlib: 2D plotting library
▫ tensorflow: open source software library for machine intelligence
▫ learn: Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
▫ mse: "mean squared error" as evaluation metric
▫ lstm_predictor: our lstm class
Page 39[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.contrib import learn
from sklearn.metrics import mean_squared_error,
mean_absolute_error
from lstm_predictor import generate_data, lstm_model

40. Case study #1: sine function
 Parameter definitions
▫ LOG_DIR: log file
▫ TIMESTEPS: RNN time steps
▫ RNN_LAYERS: RNN layer information
▫ DENSE_LAYERS: Size of DNN[10, 10]: Two dense layer with 10 hidden units
▫ TRAINING_STEPS
▫ BATCH_SIZE
▫ PRINT_STEPS
Page 40[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
LOG_DIR = './ops_logs'
TIMESTEPS = 5
RNN_LAYERS = [{'steps': TIMESTEPS}]
DENSE_LAYERS = [10, 10]
TRAINING_STEPS = 100000
BATCH_SIZE = 100
PRINT_STEPS = TRAINING_STEPS / 100

41. Case study #1: sine function
 Generate waveform
▫ fct: function
▫ x: observation
▫ time_steps: timesteps
▫ seperate: check multimodality
Page 41[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
X, y = generate_data(np.sin, np.linspace(0, 100, 10000), TIMESTEPS,
seperate=False)

42. Case study #1: sine function
 Create a regressor with TF Learn
▫ model_fn: regression model
▫ n_classes: 0 for regression
▫ verbose:
▫ steps: training steps
▫ optimizer: ("SGD", "Adam", "Adagrad")
▫ learning_rate
▫ batch_size
Page 42[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
regressor =
learn.TensorFlowEstimator(model_fn=lstm_model(TIMESTEPS,
RNN_LAYERS, DENSE_LAYERS), n_classes=0, verbose=1,
steps=TRAINING_STEPS, optimizer='Adagrad', learning_rate=0.03,
batch_size=BATCH_SIZE)

43. Case study #1: sine function
Page 43[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
validation_monitor = learn.monitors.ValidationMonitor(
X['val'], y['val'], every_n_steps=PRINT_STEPS,
early_stopping_rounds=1000)
regressor.fit(X['train'], y['train'],
monitors=[validation_monitor], logdir=LOG_DIR)
predicted = regressor.predict(X['test'])
mse = mean_squared_error(y['test'], predicted)
print ("Error: %f" % mse)
Error: 0.000294

44. Case study #1: sine function
Page 44[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
plot_predicted, = plt.plot(predicted, label='predicted')
plot_test, = plt.plot(y['test'], label='test')
plt.legend(handles=[plot_predicted, plot_test])

45. Contents
 Overview of TensorFlow
 Recurrent Neural Networks (RNN)
 RNN Implementation
 Case studies
▫ Case study #1: sine function
▫ Case study #2: electricity price forecasting
 Conclusions
 Q & A
Page 45[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

46. Energy forecasting problems
Current timeEnergy signal
(e.g. load, price, generation)
Signal forecast
External signal
(e.g. Weather) External forecast
(e.g. Weather forecast)
Page 46[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

47. Electricity Price Forecasting (EPF)
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 47
Current timeEnergy signal (Price)
External signal
(e.g. Weather, load, generation)

48. EEM2016: Price Forecasting Competition
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 48

49. MIBEL: Iberian Electricity Market
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 49

50. Dataset
 Historical
Data (2015)
 Daily
Rolling
Data
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 50

51. Dataset: Historical Data (2015-16) – Prices
 Prices ( € / MWh )
▫ Hourly real electricity price for MIBEL (the Portuguese (PT) area)
▫ Duration: Jan 1st, 2015 (UTC 00:00) – Feb 2nd, 2016 (UTC 23:00)
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 51

52. Dataset: Historical Data (2015-16) – Prices
 Monthly data (Jan, 2015)
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 52

53. Dataset: Historical Data (2015-16) – Prices
date (UTC) Price
01/01/2015 0:00 48.1
01/01/2015 1:00 47.33
01/01/2015 2:00 42.27
01/01/2015 3:00 38.41
01/01/2015 4:00 35.72
01/01/2015 5:00 35.13
01/01/2015 6:00 36.22
01/01/2015 7:00 32.4
01/01/2015 8:00 36.6
01/01/2015 9:00 43.1
01/01/2015 10:00 45.14
01/01/2015 11:00 45.14
01/01/2015 12:00 47.35
01/01/2015 13:00 47.35
01/01/2015 14:00 43.61
01/01/2015 15:00 44.91
01/01/2015 16:00 48.1
01/01/2015 17:00 58.02
01/01/2015 18:00 61.01
01/01/2015 19:00 62.69
01/01/2015 20:00 60.41
01/01/2015 21:00 58.15
01/01/2015 22:00 53.6
01/01/2015 23:00 47.34
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 53

54. Electricity market
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 54

55. Case study #2: Electricity Price Forecasting
Page 55[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks
dateparse = lambda dates: pd.datetime.strptime(dates, '%d/%m/%Y %H:%M')
rawdata = pd.read_csv("./input/ElectricityPrice/RealMarketPriceDataPT.csv",
parse_dates={'timeline': ['date', '(UTC)']},
index_col='timeline', date_parser=dateparse)
X, y = load_csvdata(rawdata, TIMESTEPS, seperate=False)

56. Tensorboard: Main Graph
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 56

57. Tensorboard: RNN
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 57

58. Tensorboard: DNN
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 58

59. Tensorboard: Linear Regression
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 59

60. Tensorboard: loss
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 60

61. Tensorboard: Histogram
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 61

62. Experiment results
Page 62[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

63. Experiment results
 LSTM + DNN + LinearRegression
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 63
predicted
test
hour
price
(euro/MWh)

64. Experiment results
Models Mean Absolute Error (euro/MWh)
LinearRegression 4.04
RidgeRegression 4.04
LassoRegression 3.73
ElasticNet 3.57
LeastAngleRegression 6.27
LSTM+DNN+LinearRegression 2.13
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 64

65. Competition Ranking (Official)
 Check the website of EPF2016 competition
▫ http://complatt.smartwatt.net/
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 65

66. Contents
 Overview of TensorFlow
 Recurrent Neural Networks (RNN)
 RNN Implementation
 Case studies
▫ Case study #1: sine function
▫ Case study #2: electricity price forecasting
 Conclusions
 Q & A
Page 66[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

67. Implementation issues
 Issues for Future Works
▫ About mathematical models
• It was used wind or solar generation forecast models?
• It was used load generation forecast models?
• It was used ensemble of mathematical models or ensemble average of multiple runs?
▫ About information used
• There are a cascading usage of the forecast in your price model? For instance, you use your
forecast (D+1) as input for model (D+2)?
• You adjusted the models based on previous forecasts of other forecasters ? If yes, whish
forecast you usually follow?
▫ About training period
• What time period was used to train your model?
• The model was updated with recent data?
• In which days you update the models?
Page 67[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

68. Contents
 Overview of TensorFlow
 Recurrent Neural Networks (RNN)
 RNN Implementation
 Case studies
▫ Case study #1: sine function
▫ Case study #2: electricity price forecasting
 Conclusions
 Q & A
Page 68[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks

69. Q & A
Any Questions?
[TensorFlow-KR Advanced Track] Electricity Price Forecasting with Recurrent Neural Networks Page 69